203 citations · 334 across the 22 of their papers we have counts for
5 papers · 1 filter
Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning
Mustafa Shukor, Alexandre Rame, Corentin Dancette +1
Following the success of Large Language Models (LLMs), Large Multimodal Models (LMMs), such as the Flamingo model and its subsequent competitors, have started to emerge as natural…
UnIVAL: Unified Model for Image, Video, Audio and Language Tasks
Mustafa Shukor, Corentin Dancette, Alexandre Rame +1
Large Language Models (LLMs) have made the ambitious quest for generalist agents significantly far from being a fantasy. A key hurdle for building such general models is the divers…
Improving Selective Visual Question Answering by Learning from Your Peers
Corentin Dancette, Spencer Whitehead, Rishabh Maheshwary +5
Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains underexplored. Recent work has shown that VQA models, out-of-the-…
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Alexandre Ramé, Guillaume Couairon, Mustafa Shukor +4
Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further a…
eP-ALM: Efficient Perceptual Augmentation of Language Models
Mustafa Shukor, Corentin Dancette, Matthieu Cord
Large Language Models (LLMs) have so far impressed the world, with unprecedented capabilities that emerge in models at large scales. On the vision side, transformer models (i.e., V…